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Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use

Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse o...

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Autores principales: Kim, Hodam, Kim, Laehyun, Im, Chang-Hwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6719101/
https://www.ncbi.nlm.nih.gov/pubmed/31395802
http://dx.doi.org/10.3390/s19163475
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author Kim, Hodam
Kim, Laehyun
Im, Chang-Hwan
author_facet Kim, Hodam
Kim, Laehyun
Im, Chang-Hwan
author_sort Kim, Hodam
collection PubMed
description Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a user were selected based on the physiological data of the user that were recorded on the same day, the effectiveness of the reuse of the machine learning model constructed during the previous experiments, without any further calibration sessions, was still questionable. This “high test-retest reliability” characteristic is of importance for the practical use of the craving detection system because the system needs to be repeatedly applied to the treatment processes as a tool to monitor the efficacy of the treatment. We presented short video clips of three addictive games to nine participants, during which various physiological signals were recorded. This experiment was repeated with different video clips on three different days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coefficient. Then, we classified whether each participant experienced a craving for gaming in the third experiment using various classifiers—the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN, linear discriminant analysis, and random forest—trained with the physiological signals recorded during the first or second experiment. Consequently, the craving/non-craving states in the third experiment were classified with an accuracy that was comparable to that achieved using the data of the same day; thus, demonstrating a high test-retest reliability and the practicality of our craving detection method. In addition, the classification performance was further enhanced by using both datasets of the first and second experiments to train the classifiers, suggesting that an individually customized game craving detection system with high accuracy can be implemented by accumulating datasets recorded on different days under different experimental conditions.
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spelling pubmed-67191012019-09-10 Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use Kim, Hodam Kim, Laehyun Im, Chang-Hwan Sensors (Basel) Article Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a user were selected based on the physiological data of the user that were recorded on the same day, the effectiveness of the reuse of the machine learning model constructed during the previous experiments, without any further calibration sessions, was still questionable. This “high test-retest reliability” characteristic is of importance for the practical use of the craving detection system because the system needs to be repeatedly applied to the treatment processes as a tool to monitor the efficacy of the treatment. We presented short video clips of three addictive games to nine participants, during which various physiological signals were recorded. This experiment was repeated with different video clips on three different days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coefficient. Then, we classified whether each participant experienced a craving for gaming in the third experiment using various classifiers—the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN, linear discriminant analysis, and random forest—trained with the physiological signals recorded during the first or second experiment. Consequently, the craving/non-craving states in the third experiment were classified with an accuracy that was comparable to that achieved using the data of the same day; thus, demonstrating a high test-retest reliability and the practicality of our craving detection method. In addition, the classification performance was further enhanced by using both datasets of the first and second experiments to train the classifiers, suggesting that an individually customized game craving detection system with high accuracy can be implemented by accumulating datasets recorded on different days under different experimental conditions. MDPI 2019-08-09 /pmc/articles/PMC6719101/ /pubmed/31395802 http://dx.doi.org/10.3390/s19163475 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Hodam
Kim, Laehyun
Im, Chang-Hwan
Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
title Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
title_full Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
title_fullStr Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
title_full_unstemmed Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
title_short Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
title_sort machine-learning-based detection of craving for gaming using multimodal physiological signals: validation of test-retest reliability for practical use
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6719101/
https://www.ncbi.nlm.nih.gov/pubmed/31395802
http://dx.doi.org/10.3390/s19163475
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